The formal and informal attempts or efforts that all companies make to handle and analyze information critical to the operation of their business may be referred to collectively as Knowledge Management. Where actionable information about factors in the market environment that could affect a company's competiveness is involved, these efforts are often more specifically referred to as Competitive Intelligence (CI) or Business Intelligence (BI).
One goal of these efforts is to link apparently unrelated signals, events, and perceptions into patterns and trends concerning the environment in which a company operates. This is a practice which is performed, to the extent possible, by senior executives and analysts in a company to better understand how the company's products or services may be more effectively positioned in the marketplace.
Rapid developments in information technology have had a significant impact on the competitive environment in which companies operate. These developments have made data critical to CI, BI, and decision-making more accessible to participants in the marketplace. However, while companies are facing more pressing needs for timely, useful information, the overabundance of available data has made it increasingly difficult for companies and their decision-makers to sift through the data and to extract that which might be relevant to them.
CI and BI are particularly important in the area of mergers and acquisitions (M&A), for example. Increasing market pressure and competition have given rise to both pressure for growth and to valuable opportunities for many companies to acquire desirable assets of other companies. Unfortunately, many opportunities may fail to deliver significant value to shareholders of the acquiring company. Many opportunities are never seen as such or acted on, as those opportunities are not known or identified as opportunities until it is too late for effective action. At the same time, there may not be an opportunity for a full and proper understanding of the ramifications of a proposed acquisition in the limited time frame in which a decision often needs to be made.
In these situations, there is typically a high dependence on readily available information, and how that information is used to forecast the fit of the acquired company and the value of its assets. The ability of a company to identify an attractive target before its competitors, or to find value where its competitors might not, is critical to a successful acquisition.
Traditionally, many companies that are looking to grow have gathered information that may be required to achieve these objectives through informal processes. These may consist of monitoring public sources (e.g. the Internet), using consultants, talking to brokers and investment bankers, or informally sharing information internally through meetings or electronic mail, for example.
A more sophisticated method of gathering the required information is to use an application such as Lotus Notes™ that permits a group of users to share information and insights with each other. Other software applications, known as groupware applications, facilitate group interaction and may be used to support more or less formalized efforts to procure potentially useful information from employees of a company.
A number of commercially available generic intelligence applications have been developed that attempt to make the intelligence process more efficient than traditional methods. For example, Knowledge.Works™ and Wisdom Builder™ are applications that are used to collect and report information for companies. The problem with these generic intelligence applications is that some key areas of the intelligence process are not effectively addressed, including in particular, the effective solicitation of information from employees on an ongoing basis, the placing of that information in a context that would make its importance more apparent, and the analysis of the solicited information to make it relevant for action.
One improved system developed by the assignee of the present invention, referred to as the Acquisitions Intelligence System v.2.1 (AIS), attempted to resolve some of the inefficiencies of prior art applications and systems. AIS is a knowledge management system designed specifically for the mining industry. AIS aids in collecting and collating information, and was primarily designed to use that information in determining whether a target project, target project holding, or target company in the mining industry may be a potential candidate for a merger or acquisition. This information is collected from employees of the company utilizing AIS, and in addition to technical information on a target company or entity (e.g. the target of a potential acquisition), the information includes other forms of other information such as rumors or gossip. In addition to the information collected by employees, AIS also obtains information from the Metals Economics Group's MineSearch™ database, an industry standard for information on current mining projects.
Furthermore, AIS utilizes a checklist of situations or developments for each target project holding, or target company under analysis, to serve as clues as to their suitability or level of desirability as a candidate for acquisition. These checklists are developed by the designer of AIS, and are preprogrammed into AIS. AIS does not permit users to modify the checklists.
AIS has limited applicability in industries other than mining, as the checklists used by AIS relate strictly to the task of identifying a suitable candidate in the mining industry for a merger or acquisition. While the designer of AIS takes into consideration events or other clues that he believes may be critical to the process of identifying suitable acquisition candidates in the mining industry in creating the checklists for AIS, no well-defined methodology or algorithm is used to create these checklists. Since such a defined methodology is not used to create the checklists, it is extremely difficult, if not impossible to modify AIS, which is designed for M&A applications in the mining industry, for use in other dissimilar applications. AIS is also limited in the sources of information it relies upon, namely employees of the user company, and a database populated specifically with data on mining projects.
Thus, there exists a need for an effective intelligence system that may be used in a variety of industries, and that it is not limited for use in the mining industry. Such a system would deal with the analysis of information on a variety of business issues, and would not be restricted to mergers and acquisitions applications. The present invention is designed to overcome at least some of the deficiencies of the above prior art systems.